MNIST

1. Network architecture same as Matrix Capsules with EM Routing Figure 1 with A=B=C=D=E=32, iteration=1.

   • Spread loss only, no reconstruction loss.

   • Adam Optimizer, learning rate default 0.001, no learning rate decay.

   • Batch size 24 (due to limit of GPU memory), iteration 1.

   • GPU: half K80 12GB memory, 2s-3s per training step.

   • Step: 43942, Test Accuracy: 99.37%.

     

     Remark: Because of allow_smaller_final_batch=False and batch_size=24, test is running on a random sample 9984 of 10000, so worse case test accuracy could be 99.21%. Modify the src/datasets/mnist.py and src/test.py to run test on full test dataset.

2. Network architecture same as Matrix Capsules with EM Routing Figure 1 with A=B=C=D=E=32, iteration=2, spread loss only, no reconstruction loss.

3. Network architecture same as Matrix Capsules with EM Routing Figure 1 with A=B=C=D=E=32, iteration=2, spread loss and reconstruction loss.

4. Network architecture 3x3 conv, 3x3 conv, capsules_init(), capsules_conv() x2, capsules_fc(), iteration=2, spread loss only, no reconstruction loss.